# Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning

arXiv: https://arxiv.org/abs/2011.06754 · HTML: https://mlsanity.com/paper/2011.06754/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs](https://mlsanity.com/paper/2106.15684/) (2021)
- [Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection](https://mlsanity.com/paper/2205.00620/) (2022)
- [Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives](https://mlsanity.com/paper/2301.10761/) (2023)
- [Learning to generate and corr- uh I mean repair language in real-time](https://mlsanity.com/paper/2308.11683/) (2023)

## Abstract (arXiv)

We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging, and utterance segmentation in a simple deep recurrent setting. We show that these tasks provide positive inductive biases to each other with the optimal contribution of each one relying on the severity of the noise from the task. Our live multi-task model outperforms similar individual tasks, delivers competitive performance, and is beneficial for future use in conversational agents in psychiatric treatment.